Kai Arulkumaran Kaixhin

## timeseries.lua
--[[
--   Element-Research Torch RNN Tutorial for recurrent neural nets : let's predict time series with a laptop GPU
--   https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html
--]]

--[[
--   Part 1
--]]

require 'rnn'

## cudnn.lua
require 'cunn'
local cudnn = require 'cudnn'

local X = torch.rand(32, 3, 24, 24):cuda()
local Y = torch.ones(32):cuda()

local net = nn.Sequential()
net:add(cudnn.SpatialConvolution(3, 8, 5, 5))
net:add(nn.View(8*20*20))
net:add(nn.Linear(8*20*20, 10))

## guidelines.md

      
              1 file
            
          
              2 forks
            
          
              4 comments
            
          
              20 stars
            
          
                Kaixhin
                / guidelines.md
            
            
              Last active
              May 12, 2022 05:29
            
              
                Student Projects
              
          
    I supervise undergraduate/postgraduate/UROP projects as part of BICV. The PI, Dr Anil Bharath, has a nice set of FAQs for prospective research students, which should give you an idea of what our group specialises in. My topic of research is deep reinforcement learning, which is less focused on computer vision and more on general machine learning or even artificial intelligence. Note that I only supervise students at Imperial College London, so please do not contact me about supervision otherwise.
I expect students to be a) highly motivated and b) technically proficient.
a) Projects that I supervise revolve around cutting-edge research, and specifically deep learning. Projects can, and have in the past, relied on research released during the course of the project. Some parts of machine learning can be found in optional modules in bioengineering courses, but (modern) deep learning is currently not taught at Imperial (as far as I am aware). I usually give crash

  
## library.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                Kaixhin
                / library.md
            
            
              Last active
              March 24, 2019 20:44
            
              
                Manga Vol. 1 Library
              
          
    Description

I collect the first volumes (and first volumes only) of various manga series (in English) in new condition.
Wishlist

I have an Amazon wishlist, but volumes from other series that I do not have are also welcome. New books only please.
Library


## matlab.sh
# Run MATLAB file and output to file
matlab -nodisplay -r <function> [<params>...] > <file>
# Email output file once done
mutt -s "Results" <email> < <file>

## maintenance.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                Kaixhin
                / maintenance.md
            
            
              Last active
              July 30, 2016 07:51
            
              
                Maintenance Schedule: Weekly and monthly checklists, as well as automatic maintenance
              
          
    Weekly Maintenance

Windows 8.1


 CCleaner
 Check Disk
 System Image Backup

OS X 10.9 Mavericks


 CCleaner
 Verify Disk
	--[[
	-- Element-Research Torch RNN Tutorial for recurrent neural nets : let's predict time series with a laptop GPU
	-- https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html
	--]]

	--[[
	-- Part 1
	--]]

	require 'rnn'
	require 'cunn'
	local cudnn = require 'cudnn'

	local X = torch.rand(32, 3, 24, 24):cuda()
	local Y = torch.ones(32):cuda()

	local net = nn.Sequential()
	net:add(cudnn.SpatialConvolution(3, 8, 5, 5))
	net:add(nn.View(82020))
	net:add(nn.Linear(82020, 10))
	# Run MATLAB file and output to file
	matlab -nodisplay -r <function> [<params>...] > <file>
	# Email output file once done
	mutt -s "Results" <email> < <file>